Description Usage Arguments Details Value See Also Examples

Finds all links or pairs of clusters within a certain distance of each other and then calculates the correlation between them. The links found can be restricted to only be between two classes, for example TSSs to enhancers.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ```
findLinks(object, ...)
## S4 method for signature 'GRanges'
findLinks(object, maxDist = 10000L, directional = NULL)
## S4 method for signature 'RangedSummarizedExperiment'
findLinks(
object,
inputAssay,
maxDist = 10000L,
directional = NULL,
corFun = stats::cor.test,
vals = c("estimate", "p.value"),
...
)
``` |

`object` |
GRanges or RangedSummarizedExperiment: Clusters, possibly with expression for calculating correlations. |

`...` |
additional arguments passed to methods or ultimately corFun. |

`maxDist` |
integer: Maximum distance between links. |

`directional` |
character: Name of a column in object holding a grouping of the clusters. This must be a factor with two levels. The first level is used as the basis for calculating orientation (see below). |

`inputAssay` |
character: Name of assay holding expression values (if object is a RangedSummarizedExperiment) |

`corFun` |
function: Function for calculating pairwise correlations. See notes for supplying custom functions. |

`vals` |
character: Statistics extracted from the results produced by corFun. See notes for supplying custom functions. |

A custom function for calculation correlations can be supplied by the user. The output of this function must be a named list or vector of numeric values. The names of the vals to be extracted should be supplied to vals.

A GInteractions holding the links, along with the distance between them and correlation estimate and p-value calculated from their expression. If a directional analysis was performed, the two anchors are always connecting members of the two classes and the orientation of the second anchor relative to the first is additionaly calculated (e.g. whether an enhancers is upstream or downstream of the TSS).

Other Spatial functions:
`findStretches()`

,
`trackLinks()`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ```
library(InteractionSet)
# Subset to highly expressed unidirectional clusters
TCs <- subset(exampleUnidirectional, score > 10)
# Find links within a certain distance
findLinks(TCs, inputAssay="counts", maxDist=10000L)
# To find TSS-to-enhancer type links, first merge the clusters:
colData(exampleBidirectional) <- colData(TCs)
rowRanges(TCs)$clusterType <- "TSS"
rowRanges(exampleBidirectional)$clusterType <- "Enhancer"
SE <- combineClusters(TCs, exampleBidirectional, removeIfOverlapping="object1")
rowRanges(SE)$clusterType <- factor(rowRanges(SE)$clusterType, levels=c("TSS", "Enhancer"))
# Calculate kendall correlations of TPM values:
SE <- calcTPM(SE, totalTags="totalTags")
findLinks(SE, inputAssay="TPM", maxDist=10000L, directional="clusterType", method="kendall")
``` |

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